With continuous miniaturization of silicon technology and proliferation of consumer and cell-phone cameras, there has been an exponential increase in the number of images that are captured. Whether the images are stored on personal computers or reside on social networks (e.g. Instagram, Flickr), the sheer number of images calls for methods to determine various image properties, such as object presence or appeal for the purpose of automatic image management. One of the central problems in consumer photography centers on determining the aesthetic appeal of the image. The problem itself is challenging, because the overall aesthetic value of an image is dependent on its technical quality, composition, emotional value, and the like. In order to combine all of these aspects, sophisticated systems must be built to take into account all of the aspects of image aesthetics. One such aspect is the prediction of the presence and possible segmentation of a salient object in the image, which could inform the system about the features that the system should consider in determining the aesthetics appeal.
An efficient method for the computation of salient foreground in consumer quality images has recently been proposed in F. Perazzi, O. Sorkine-Hornung, A. Sorkine-Hornung, “Efficient Salient Foreground Detection for Images and Video using Fiedler Vectors,” Eurographics Workshop on Intelligent Cinematography and Editing, Zurich, Switzerland, May 5, 2015. However, this method is not able to deal with an image that contains multiple objects in the scene effectively. Specifically, in the case of multiple disconnected objects, this method can only correctly detect a single salient object in the scene. What is desired is a system and method that can overcome this major deficiency of the prior art method.